System

Abstract

To provide a system for proposing an optimal education method on the basis of a personality of a child and an education principle of parents of the child and instructing.SOLUTION: A system comprises: a collection part; an analysis part; a proposal part; and an instruction part. The collection part collects information of a child and information of parents of the child. The analysis part analyzes the information collected by the collection part. The proposal part proposes an education method applied for the child on the basis of an analysis result acquired by the analysis part. The instruction part performs instruction on the basis of the education method proposed by the proposal part.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately propose optimal educational methods based on a child's personality and the parents' educational preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to propose and provide optimal educational methods based on the child's personality and the parent's educational principles. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and an instruction unit. The collection unit collects information on the child and the parent. The analysis unit analyzes the information collected by the collection unit. The suggestion unit suggests an educational method to be applied to the child based on the analysis result obtained by the analysis unit. The instruction unit provides instruction based on the educational method suggested by the suggestion unit. Effect of the Invention

[0007] The system according to the embodiment can suggest optimal educational methods based on the child's personality and the parents' educational principles, and provide instruction. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The education suggestion system according to the embodiment of the present invention is a system that proposes an educational method for a child in a form that matches the educational policy of the parents, and provides guidance based on the educational method. This education suggestion system collects information on the child and the parent, analyzes the collected information, proposes an optimal educational method, and provides guidance. For example, behavioral data and interview results of the child and the parent are collected and analyzed using a machine learning algorithm to propose an optimal educational method based on the child's personality, individuality, interests, learning style, and the parent's educational policy. Guidance is provided based on the proposed educational method, the child's reaction is monitored during the guidance process, and a change in the educational method is proposed as necessary. For example, the optimal one can be selected from various educational methods such as the Montessori educational method, the Stienner educational method, the Constructivist educational method, and the Regio Emilia educational method. This can promote the growth and development of the child and provide education that matches the parent's educational policy. This allows the education suggestion system to propose an optimal educational method based on the child's personality and interests, and the parent's educational policy, and provide guidance.

[0029] The education suggestion system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a guidance unit. The collection unit collects information on the child and the parent. The information on the child and the parent includes, but is not limited to, for example, age, gender, learning history, interests, and home environment. The collection unit collects information, for example, through questionnaires and interviews. The collection unit can also collect behavioral data on the child and the parent using a sensor. For example, the collection unit monitors the child's behavioral patterns and learning behavior with a sensor and collects data. The analysis unit analyzes the collected information using a machine learning algorithm. For example, statistical analysis and neural networks are used for the analysis, but are not limited to such examples. For example, the analysis unit analyzes the child's personality and interests based on the collected data, and analyzes the parent's educational policy. The suggestion unit determines an appropriate educational method to be applied to the child from among multiple types of educational methods based on the analysis result obtained by the analysis unit. The suggestion unit proposes an optimal educational method based on, for example, the child's learning style and interests. The guidance unit provides guidance based on the educational method proposed by the suggestion unit. The teaching unit can provide, for example, individual or group instruction. The teaching unit can also provide online instruction. The child's response is monitored during the instruction process, and feedback is provided to the suggestion unit as necessary to suggest changes to the education method. In this way, the education suggestion system according to the embodiment can suggest the optimal education method based on the child's personality and interests, and the parent's education policy, and provide instruction.

[0030] The collection unit collects information on the child and the parent. The information on the child and the parent includes, but is not limited to, for example, age, gender, learning history, interests, and home environment. The collection unit collects information, for example, through questionnaires and interviews. Specifically, detailed information such as the child's learning habits, the parent's educational policy, and the home learning environment is collected through online questionnaires and face-to-face interviews. The collection unit can also collect behavioral data on the child and the parent using sensors. For example, the collection unit monitors the child's behavioral patterns and learning behavior with sensors and collects data. Specifically, data such as the child's learning time, concentration level, and break time is collected in real time using a wearable device or a camera or microphone installed in the home. This allows the collection unit to grasp the child's learning behavior and the parent's educational policy in detail and collect a wealth of data to provide to the analysis unit. Furthermore, the collection unit can centrally manage the collected data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis unit and proposal unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit analyzes the collected information using a machine learning algorithm. For example, statistical analysis and neural networks are used for the analysis, but are not limited to such examples. Specifically, the child's personality and interests are analyzed based on the collected data, and the parent's educational policy is analyzed. For example, statistical analysis is used to identify the child's learning progress and strengths and weaknesses from the child's learning history and behavioral patterns. In addition, a neural network is used to analyze the child's interests and personality traits through deep learning to clarify individual educational needs. Furthermore, the parent's educational policy and home environment are also analyzed to comprehensively evaluate factors that affect the child's learning. This allows the analysis unit to quickly and accurately analyze the collected data and provide the suggestion unit with the optimal educational method based on the child's learning style and the parent's educational policy. Furthermore, the analysis unit can also perform long-term learning effects and trend analysis using past data and statistical information. For example, the effectiveness of a specific educational method is evaluated based on past learning data, and a future educational policy is proposed. In addition, the analysis unit can use an anomaly detection algorithm to detect unusual learning patterns and abnormal data and issue an early warning. This enables the analysis unit to not only grasp the situation in real time, but also evaluate the effects of long-term learning and detect anomalies, improving the reliability and effectiveness of the entire system.

[0032] The suggestion unit determines an appropriate educational method to be applied to the child from among multiple educational methods based on the analysis results obtained by the analysis unit. Specifically, the optimal educational method is proposed based on the child's learning style and interests. For example, if the child prefers visual learning, an educational method using visual teaching materials and videos is proposed, and if the child prefers auditory learning, audio teaching materials and voice instruction are proposed. In addition, based on the child's interests, educational materials related to a specific theme or topic are selected to increase the child's motivation to learn. Furthermore, the suggestion unit can also propose a learning support method at home and activities for parents and children to engage in, taking into account the parent's educational policy and home environment. This allows the suggestion unit to provide an optimal educational method according to the individual needs of the child and maximize the learning effect. Furthermore, the suggestion unit can continuously monitor the effect of the proposed educational method and review and improve the educational method as necessary. For example, if the proposed educational method does not produce the expected effect, a new educational method is proposed based on feedback from the analysis unit. Furthermore, the suggestion unit can incorporate the latest educational research and technology and always provide the most advanced educational method. This allows the suggestion unit to maximize the learning effect of the child and provide continuous learning support.

[0033] The teaching department provides instruction based on the educational method proposed by the proposal department. Specifically, individual instruction and group instruction can be provided. For example, in individual instruction, customized instruction is provided according to the learning progress and level of understanding of each child, and in group instruction, children with the same interests and learning levels are gathered together to improve the learning effect through collaborative learning and discussion. The teaching department can also provide online instruction. In online instruction, high-quality education can be provided to children in remote locations using video calls and chat functions. During the instruction process, the child's response is monitored, and feedback is provided to the proposal department as necessary, and a change in the educational method is proposed. For example, if a child is not interested in a particular teaching material or does not progress in understanding, the teaching department conveys that information to the proposal department and requests it to propose a new educational method. This allows the teaching department to provide flexible instruction according to the child's learning situation and maximize the learning effect. In addition, the teaching department can provide advice on how to support learning at home and activities for parents and children to engage in through communication with parents. This allows the teaching department to comprehensively support the learning environment at school and at home and maximize the learning effect of children.

[0034] The collection unit can collect behavioral data and interview results of the child and the parent. The collection unit collects behavioral data such as, for example, the daily behavioral patterns, learning behavior, and play behavior of the child and the parent. For example, the collection unit uses a sensor to monitor the child's behavior and collect data. The collection unit can also collect detailed information about the child and the parent through interviews. For example, the collection unit conducts face-to-face interviews or online interviews to collect information about the personality, interests, and educational policies of the child and the parent. This allows the collection unit to obtain detailed information about the child and the parent. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input behavioral data acquired by a sensor to the generation AI and have the generation AI perform analysis of the data.

[0035] The suggestion unit can determine an appropriate educational method to be applied to the child from among multiple educational methods based on the analysis result. The suggestion unit proposes an optimal educational method based on, for example, the child's learning style and interests. For example, the suggestion unit selects an optimal educational method for the child from educational methods such as active learning, project-based learning, and flipped classroom. The suggestion unit determines an optimal educational method based on the analysis result obtained by the analysis unit, taking into account the child's personality and interests, and the parent's educational policy. This allows the suggestion unit to propose an optimal educational method for the child. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the data obtained by the analysis unit to the generation AI and cause the generation AI to select the optimal educational method.

[0036] The teaching unit monitors the child's reaction during the teaching process, and if necessary, provides feedback to the suggestion unit and can propose a change in the teaching method. The teaching unit, for example, monitors the child's facial expressions, behavior, remarks, etc., and observes the child's reaction. For example, the teaching unit can observe how the child reacts to a specific teaching method and change the method as appropriate. The teaching unit provides feedback to the suggestion unit based on the child's reaction and proposes a change in the teaching method. This allows the teaching unit to provide the child with the most suitable education. Some or all of the above-mentioned processing in the teaching unit may be performed, for example, using AI, or may be performed without using AI. For example, the teaching unit can input the child's reaction data into the generation AI and have the generation AI execute a proposal to change the teaching method.

[0037] The analysis unit can analyze the collected information using a machine learning algorithm. The analysis unit analyzes the information using a machine learning algorithm such as statistical analysis, neural network, or support vector machine. For example, the analysis unit analyzes the personality and interests of the child and the parent's educational policy based on the collected data. This allows the analysis unit to analyze the collected information with high accuracy. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and cause the generation AI to analyze the data.

[0038] The analysis unit can analyze the child's character, personality, and interests, and the parent's character, personality, and educational policy based on the information collected by the collection unit. The analysis unit can analyze the child's character and interests, for example, using a personality diagnostic test or an interest questionnaire. The analysis unit can also analyze the parent's educational philosophy, goals, values, and the like. For example, the analysis unit can analyze the child's character and interests and the parent's educational policy in detail based on the collected information. This allows the analysis unit to analyze detailed information about the child and the parent and provide basic data for proposing an optimal educational method. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and have the generation AI execute the analysis of the data.

[0039] The collection unit can analyze the changes in the parent's educational policy in the past and select the optimal information collection method. For example, the collection unit lists the educational methods that the parent has tried in the past and preferentially collects the successful methods. The collection unit can also analyze the changes in the parent's educational policy in chronological order and select the information collection method most suitable for the current policy. Furthermore, the collection unit can avoid educational methods that the parent has rejected in the past and collect information that suits the parent's preferences. In this way, the collection unit can analyze the changes in the parent's educational policy in the past and select the optimal information collection method. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input data on the parent's past educational policy into the generation AI and cause the generation AI to select the optimal information collection method.

[0040] The collection unit can filter information based on the child's current learning situation and field of interest when collecting information. For example, the collection unit preferentially collects information related to the field in which the child is currently interested. The collection unit can also filter necessary information according to the child's learning progress and collect it at an appropriate time. Furthermore, when the child's interests change, the collection unit can quickly update the information to be collected to respond to the latest interests. This allows the collection unit to filter information based on the child's current learning situation and field of interest and collect more appropriate information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input data on the child's learning situation and field of interest to the generation AI and cause the generation AI to filter the information.

[0041] When collecting information, the collection unit can preferentially collect highly relevant information taking into account the geographical location information of the parent. For example, the collection unit preferentially collects educational resources in the area where the parent lives. The collection unit can also collect information on nearby educational events and workshops based on the geographical location of the parent. Furthermore, the collection unit can also collect information on appropriate educational methods taking into account the cultural background related to the geographical location of the parent. This allows the collection unit to preferentially collect highly relevant information taking into account the geographical location information of the parent. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the geographical location data of the parent to the generation AI and cause the generation AI to collect highly relevant information.

[0042] The collection unit can analyze the parent's social media activity and collect related information when collecting information. For example, the collection unit can analyze educational resources shared by the parent on social media and collect related information. The collection unit can also collect information on educational experts and organizations followed by the parent. Furthermore, the collection unit can also collect related educational information based on topics in which the parent has shown interest on social media. This allows the collection unit to analyze the parent's social media activity and efficiently collect related information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the parent's social media data to the generation AI and cause the generation AI to collect related information.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the child's personality and interests during the analysis. For example, if the child is curious, the analysis unit performs a detailed analysis to promote deep understanding. In addition, if the child lacks concentration, the analysis unit can perform a concise, to-the-point analysis. Furthermore, if the child has a strong interest in a particular field, the analysis unit can perform a detailed analysis specialized in that field. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the child's personality and interests and provide more appropriate analysis results. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the child's personality and interests to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the parent's educational policy during analysis. For example, if the parent supports Montessori education, the analysis unit applies an analysis algorithm based on the educational policy. In addition, if the parent supports Stiener education, the analysis unit can also apply an analysis algorithm based on the educational policy. In addition, if the parent supports Constructivism education, the analysis unit can also apply an analysis algorithm based on the educational policy. This allows the analysis unit to apply different analysis algorithms depending on the category of the parent's educational policy and provide more appropriate analysis results. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the parent's educational policy data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the submission time of the collected information during the analysis. For example, the analysis unit can give priority to analyzing the latest information and provide the results quickly. The analysis unit can also give priority to analyzing important information and provide detailed results. Furthermore, the analysis unit can postpone analyzing old information and give priority to analyzing the latest information. This allows the analysis unit to determine the priority of the analysis based on the submission time of the collected information and provide the analysis results quickly. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the collected information to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the collected information during analysis. For example, the analysis unit preferentially analyzes information related to the child's personality and interests. The analysis unit can also preferentially analyze information related to the parent's educational policy. Furthermore, the analysis unit can also preferentially analyze information related to the child's learning style. This allows the analysis unit to adjust the order of analysis based on the relevance of the collected information and provide a more appropriate analysis result. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the collected information to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] The suggestion unit can adjust the level of detail of the proposal based on the importance of the educational method at the time of suggestion. For example, the suggestion unit makes a detailed proposal for an important educational method. The suggestion unit can also make a concise proposal for an educational method with a low importance. Furthermore, the suggestion unit can make a detailed proposal for an educational method that has a significant effect on the growth of a child. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of the educational method and provide a more appropriate proposal. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the importance data of the educational method to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the educational method at the time of suggestion. For example, for the Montessori educational method, the suggestion unit applies a suggestion algorithm based on its characteristics. Furthermore, for the Stienner educational method, the suggestion unit can also apply a suggestion algorithm based on its characteristics. Furthermore, for the Constructivist educational method, the suggestion unit can apply a suggestion algorithm based on its characteristics. Thereby, the suggestion unit can apply different suggestion algorithms depending on the category of the educational method and provide a more appropriate suggestion. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input category data of the educational method to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0049] At the time of proposal, the suggestion unit can determine the priority of the proposal based on the submission time of the educational method. For example, the suggestion unit preferentially proposes the latest educational method. The suggestion unit can also preferentially propose important educational methods. Furthermore, the suggestion unit can postpone old educational methods and preferentially propose the latest educational methods. In this way, the suggestion unit can determine the priority of the proposal based on the submission time of the educational method and quickly provide appropriate proposals. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the submission time data of the educational method to the generation AI and cause the generation AI to execute the determination of the priority of the proposal.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the educational methods when making suggestions. For example, the suggestion unit preferentially suggests educational methods related to the child's personality and interests. The suggestion unit can also preferentially suggest educational methods related to the parent's educational policy. Furthermore, the suggestion unit can also preferentially suggest educational methods related to the child's learning style. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the educational methods and provide more appropriate suggestions. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input relevance data of educational methods to the generation AI and cause the generation AI to adjust the order of suggestions.

[0051] During instruction, the instruction unit can select the optimal instruction method by analyzing the child's past learning behavior. For example, the instruction unit preferentially incorporates into the instruction a learning method that the child has had success with in the past. The instruction unit can also avoid learning methods that the child has had difficulty with in the past and try a different method. Furthermore, the instruction unit can analyze the child's past learning behavior and select the most effective instruction method. This allows the instruction unit to analyze the child's past learning behavior and select the optimal instruction method. A part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the child's past learning behavior data into the generation AI and cause the generation AI to select the optimal instruction method.

[0052] The teaching unit can customize the means of instruction based on the child's current learning situation during instruction. The teaching unit, for example, adjusts the means of instruction to match the content that the child is currently learning. The teaching unit can also adjust the pace of instruction according to the child's learning progress. Furthermore, the teaching unit can select optimal teaching materials and resources based on the child's current learning situation. This allows the teaching unit to customize the means of instruction based on the child's current learning situation and provide more effective instruction. Some or all of the above-mentioned processing in the teaching unit may be performed, for example, using AI or may be performed without using AI. For example, the teaching unit can input the child's current learning situation data into the generation AI and cause the generation AI to customize the means of instruction.

[0053] The teaching unit can select the optimal teaching method by taking into account the child's geographical location information during teaching. The teaching unit, for example, uses educational resources in the area where the child lives to provide teaching. The teaching unit can also incorporate nearby educational events and workshops into the teaching based on the child's geographical location. Furthermore, the teaching unit can select an appropriate teaching method by taking into account the cultural background related to the child's geographical location. This allows the teaching unit to select the optimal teaching method by taking into account the child's geographical location information. Some or all of the above-mentioned processing in the teaching unit may be performed, for example, using AI or may be performed without using AI. For example, the teaching unit can input the child's geographical location data into the generation AI and cause the generation AI to select the optimal teaching method.

[0054] During instruction, the guidance unit can analyze the child's social media activity and suggest teaching methods. The guidance unit can, for example, analyze the interests and concerns shared by the child on social media and suggest related teaching methods. The guidance unit can also incorporate information about educational experts and organizations that the child follows into the instruction. Furthermore, the guidance unit can also suggest related teaching methods based on topics in which the child has shown interest on social media. This allows the guidance unit to analyze the child's social media activity and suggest more appropriate teaching methods. Some or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI or may be performed without using AI. For example, the guidance unit can input the child's social media data into the generation AI and have the generation AI execute the suggestion of teaching methods.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The education suggestion system can further collect health data of the child and reflect it in the suggestion of an education method. For example, the collection unit collects health data such as the child's sleep pattern, eating habits, and amount of exercise. The analysis unit can analyze the health data and suggest an optimal education method based on the child's health condition. The suggestion unit selects an education method that suits the child's physical condition, taking into account the health data. The instruction unit can adjust the timing and content of instruction based on the health data, and provide effective education while maintaining the child's health.

[0057] The education suggestion system can further collect data on the child's friendships and reflect it in the suggestion of an education method. For example, the collection unit collects data on the child's friendships and sociability. The analysis unit can analyze this data and suggest an optimal education method based on the child's sociability and friendships. The suggestion unit selects an education method that allows the child to build better human relationships, taking into account the friendship data. The instruction unit can provide guidance to foster the child's sociability by incorporating group activities and cooperative learning based on the friendship data.

[0058] The education suggestion system can further collect the stress level of the parent and reflect it in the suggestion of the education method. For example, the collection unit uses a questionnaire or a sensor to measure the stress level of the parent. The analysis unit can analyze these data and suggest the optimal education method based on the stress level of the parent. The suggestion unit selects an education method that the parent can comfortably practice, taking into account the stress level of the parent. The instruction unit can adjust the frequency and content of the instruction based on the stress level of the parent, and provide effective education while reducing the burden on the parent.

[0059] The education suggestion system can further collect data on the child's learning environment and reflect it in the suggestion of an education method. For example, the collection unit collects data on the child's learning environment. The analysis unit can analyze the data and suggest an optimal education method based on the child's learning environment. The suggestion unit selects an education method that takes into account the learning environment data to create an environment in which the child can easily concentrate. The instruction unit can adjust the method and timing of instruction based on the learning environment data to maximize the child's learning effect.

[0060] The education suggestion system can further analyze changes in the parents' educational principles and reflect them in the proposed educational methods. For example, the collection unit collects educational methods that the parents have tried in the past and their results. The analysis unit can analyze this data and suggest the optimal educational method based on the changes in the parents' educational principles. The suggestion unit selects the educational method that is most suitable for the current principles, taking into account the changes in the parents' educational principles. The instruction unit can adjust the method and content of instruction based on the data on the changes in the parents' educational principles, and provide effective education that matches the parents' principles.

[0061] The education suggestion system can further analyze the child's learning history and reflect it in the suggestion of an education method. For example, the collection unit collects the child's past learning history. The analysis unit can analyze these data and suggest the optimal education method based on the child's learning history. The suggestion unit takes into account the learning history data and preferentially suggests learning methods that have been successful for the child in the past. The teaching unit can adjust the teaching method and content based on the learning history data to maximize the child's learning effect.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The collection unit collects information about children and parents. The collected information includes age, gender, learning history, interests, and home environment. In addition to collecting information through questionnaires and interviews, the collection unit can also collect behavioral data about children and parents using sensors. For example, the data can be collected by monitoring children's behavioral patterns and learning behavior with sensors. Step 2: The analysis unit analyzes the collected information. Machine learning algorithms, statistical analysis, and neural networks are used for the analysis. For example, the analysis unit analyzes the child's personality and interests based on the collected data, and analyzes the parents' educational policy. Step 3: The suggestion unit determines an appropriate educational method to be applied to the child from among multiple educational methods based on the analysis results obtained by the analysis unit. The suggestion unit suggests the optimal educational method based on the child's learning style and interests. Step 4: The teaching department provides instruction based on the teaching methods proposed by the suggestion department. The teaching department can provide individual instruction, group instruction, or online instruction. In addition, the teaching department monitors the children's reactions during the instruction process, and if necessary, provides feedback to the suggestion department and proposes changes to the teaching methods.

[0064] (Example 2) The education suggestion system according to the embodiment of the present invention is a system that proposes an educational method for a child in a form that matches the educational policy of the parents, and provides guidance based on the educational method. This education suggestion system collects information on the child and the parent, analyzes the collected information, proposes an optimal educational method, and provides guidance. For example, behavioral data and interview results of the child and the parent are collected and analyzed using a machine learning algorithm to propose an optimal educational method based on the child's personality, individuality, interests, learning style, and the parent's educational policy. Guidance is provided based on the proposed educational method, the child's reaction is monitored during the guidance process, and a change in the educational method is proposed as necessary. For example, the optimal one can be selected from various educational methods such as the Montessori educational method, the Stienner educational method, the Constructivist educational method, and the Regio Emilia educational method. This can promote the growth and development of the child and provide education that matches the parent's educational policy. This allows the education suggestion system to propose an optimal educational method based on the child's personality and interests, and the parent's educational policy, and provide guidance.

[0065] The education suggestion system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a guidance unit. The collection unit collects information on the child and the parent. The information on the child and the parent includes, but is not limited to, for example, age, gender, learning history, interests, and home environment. The collection unit collects information, for example, through questionnaires and interviews. The collection unit can also collect behavioral data on the child and the parent using a sensor. For example, the collection unit monitors the child's behavioral patterns and learning behavior with a sensor and collects data. The analysis unit analyzes the collected information using a machine learning algorithm. For example, statistical analysis and neural networks are used for the analysis, but are not limited to such examples. For example, the analysis unit analyzes the child's personality and interests based on the collected data, and analyzes the parent's educational policy. The suggestion unit determines an appropriate educational method to be applied to the child from among multiple types of educational methods based on the analysis result obtained by the analysis unit. The suggestion unit proposes an optimal educational method based on, for example, the child's learning style and interests. The guidance unit provides guidance based on the educational method proposed by the suggestion unit. The teaching unit can provide, for example, individual or group instruction. The teaching unit can also provide online instruction. The child's response is monitored during the instruction process, and feedback is provided to the suggestion unit as necessary to suggest changes to the education method. In this way, the education suggestion system according to the embodiment can suggest the optimal education method based on the child's personality and interests, and the parent's education policy, and provide instruction.

[0066] The collection unit collects information on the child and the parent. The information on the child and the parent includes, but is not limited to, for example, age, gender, learning history, interests, and home environment. The collection unit collects information, for example, through questionnaires and interviews. Specifically, detailed information such as the child's learning habits, the parent's educational policy, and the home learning environment is collected through online questionnaires and face-to-face interviews. The collection unit can also collect behavioral data on the child and the parent using sensors. For example, the collection unit monitors the child's behavioral patterns and learning behavior with sensors and collects data. Specifically, data such as the child's learning time, concentration level, and break time is collected in real time using a wearable device or a camera or microphone installed in the home. This allows the collection unit to grasp the child's learning behavior and the parent's educational policy in detail and collect a wealth of data to provide to the analysis unit. Furthermore, the collection unit can centrally manage the collected data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis unit and proposal unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0067] The analysis unit analyzes the collected information using a machine learning algorithm. For example, statistical analysis and neural networks are used for the analysis, but are not limited to such examples. Specifically, the child's personality and interests are analyzed based on the collected data, and the parent's educational policy is analyzed. For example, statistical analysis is used to identify the child's learning progress and strengths and weaknesses from the child's learning history and behavioral patterns. In addition, a neural network is used to analyze the child's interests and personality traits through deep learning to clarify individual educational needs. Furthermore, the parent's educational policy and home environment are also analyzed to comprehensively evaluate factors that affect the child's learning. This allows the analysis unit to quickly and accurately analyze the collected data and provide the suggestion unit with the optimal educational method based on the child's learning style and the parent's educational policy. Furthermore, the analysis unit can also perform long-term learning effects and trend analysis using past data and statistical information. For example, the effectiveness of a specific educational method is evaluated based on past learning data, and a future educational policy is proposed. In addition, the analysis unit can use an anomaly detection algorithm to detect unusual learning patterns and abnormal data and issue an early warning. This enables the analysis unit to not only grasp the situation in real time, but also evaluate the effects of long-term learning and detect anomalies, improving the reliability and effectiveness of the entire system.

[0068] The suggestion unit determines an appropriate educational method to be applied to the child from among multiple educational methods based on the analysis results obtained by the analysis unit. Specifically, the optimal educational method is proposed based on the child's learning style and interests. For example, if the child prefers visual learning, an educational method using visual teaching materials and videos is proposed, and if the child prefers auditory learning, audio teaching materials and voice instruction are proposed. In addition, based on the child's interests, educational materials related to a specific theme or topic are selected to increase the child's motivation to learn. Furthermore, the suggestion unit can also propose a learning support method at home and activities for parents and children to engage in, taking into account the parent's educational policy and home environment. This allows the suggestion unit to provide an optimal educational method according to the individual needs of the child and maximize the learning effect. Furthermore, the suggestion unit can continuously monitor the effect of the proposed educational method and review and improve the educational method as necessary. For example, if the proposed educational method does not produce the expected effect, a new educational method is proposed based on feedback from the analysis unit. Furthermore, the suggestion unit can incorporate the latest educational research and technology and always provide the most advanced educational method. This allows the suggestion unit to maximize the learning effect of the child and provide continuous learning support.

[0069] The teaching department provides instruction based on the educational method proposed by the proposal department. Specifically, individual instruction and group instruction can be provided. For example, in individual instruction, customized instruction is provided according to the learning progress and level of understanding of each child, and in group instruction, children with the same interests and learning levels are gathered together to improve the learning effect through collaborative learning and discussion. The teaching department can also provide online instruction. In online instruction, high-quality education can be provided to children in remote locations using video calls and chat functions. During the instruction process, the child's response is monitored, and feedback is provided to the proposal department as necessary, and a change in the educational method is proposed. For example, if a child is not interested in a particular teaching material or does not progress in understanding, the teaching department conveys that information to the proposal department and requests it to propose a new educational method. This allows the teaching department to provide flexible instruction according to the child's learning situation and maximize the learning effect. In addition, the teaching department can provide advice on how to support learning at home and activities for parents and children to engage in through communication with parents. This allows the teaching department to comprehensively support the learning environment at school and at home and maximize the learning effect of children.

[0070] The collection unit can collect behavioral data and interview results of the child and the parent. The collection unit collects behavioral data such as, for example, the daily behavioral patterns, learning behavior, and play behavior of the child and the parent. For example, the collection unit uses a sensor to monitor the child's behavior and collect data. The collection unit can also collect detailed information about the child and the parent through interviews. For example, the collection unit conducts face-to-face interviews or online interviews to collect information about the personality, interests, and educational policies of the child and the parent. This allows the collection unit to obtain detailed information about the child and the parent. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input behavioral data acquired by a sensor to the generation AI and have the generation AI perform analysis of the data.

[0071] The suggestion unit can determine an appropriate educational method to be applied to the child from among multiple educational methods based on the analysis result. The suggestion unit proposes an optimal educational method based on, for example, the child's learning style and interests. For example, the suggestion unit selects an optimal educational method for the child from educational methods such as active learning, project-based learning, and flipped classroom. The suggestion unit determines an optimal educational method based on the analysis result obtained by the analysis unit, taking into account the child's personality and interests, and the parent's educational policy. This allows the suggestion unit to propose an optimal educational method for the child. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the data obtained by the analysis unit to the generation AI and cause the generation AI to select the optimal educational method.

[0072] The teaching unit monitors the child's reaction during the teaching process, and if necessary, provides feedback to the suggestion unit and can propose a change in the teaching method. The teaching unit, for example, monitors the child's facial expressions, behavior, remarks, etc., and observes the child's reaction. For example, the teaching unit can observe how the child reacts to a specific teaching method and change the method as appropriate. The teaching unit provides feedback to the suggestion unit based on the child's reaction and proposes a change in the teaching method. This allows the teaching unit to provide the child with the most suitable education. Some or all of the above-mentioned processing in the teaching unit may be performed, for example, using AI, or may be performed without using AI. For example, the teaching unit can input the child's reaction data into the generation AI and have the generation AI execute a proposal to change the teaching method.

[0073] The analysis unit can analyze the collected information using a machine learning algorithm. The analysis unit analyzes the information using a machine learning algorithm such as statistical analysis, neural network, or support vector machine. For example, the analysis unit analyzes the personality and interests of the child and the parent's educational policy based on the collected data. This allows the analysis unit to analyze the collected information with high accuracy. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and cause the generation AI to analyze the data.

[0074] The analysis unit can analyze the child's character, personality, and interests, and the parent's character, personality, and educational policy based on the information collected by the collection unit. The analysis unit can analyze the child's character and interests, for example, using a personality diagnostic test or an interest questionnaire. The analysis unit can also analyze the parent's educational philosophy, goals, values, and the like. For example, the analysis unit can analyze the child's character and interests and the parent's educational policy in detail based on the collected information. This allows the analysis unit to analyze detailed information about the child and the parent and provide basic data for proposing an optimal educational method. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and have the generation AI execute the analysis of the data.

[0075] The collection unit can estimate the child's emotions and adjust the timing of information collection based on the estimated child's emotions. For example, the collection unit interviews the child when he or she is relaxed to elicit natural answers. The collection unit can also prioritize observation and collect behavioral data when the child is excited. Furthermore, the collection unit can temporarily suspend information collection and allow the child to rest when the child is tired. This allows the collection unit to adjust the timing of information collection according to the child's emotions and collect more natural information. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the child's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0076] The collection unit can analyze the changes in the parent's educational policy in the past and select the optimal information collection method. For example, the collection unit lists the educational methods that the parent has tried in the past and preferentially collects the successful methods. The collection unit can also analyze the changes in the parent's educational policy in chronological order and select the information collection method most suitable for the current policy. Furthermore, the collection unit can avoid educational methods that the parent has rejected in the past and collect information that suits the parent's preferences. In this way, the collection unit can analyze the changes in the parent's educational policy in the past and select the optimal information collection method. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input data on the parent's past educational policy into the generation AI and cause the generation AI to select the optimal information collection method.

[0077] The collection unit can filter information based on the child's current learning situation and field of interest when collecting information. For example, the collection unit preferentially collects information related to the field in which the child is currently interested. The collection unit can also filter necessary information according to the child's learning progress and collect it at an appropriate time. Furthermore, when the child's interests change, the collection unit can quickly update the information to be collected to respond to the latest interests. This allows the collection unit to filter information based on the child's current learning situation and field of interest and collect more appropriate information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input data on the child's learning situation and field of interest to the generation AI and cause the generation AI to filter the information.

[0078] The collection unit can estimate the child's emotion and determine the priority of information to be collected based on the estimated emotion of the child. For example, when the child is excited, the collection unit preferentially collects information that attracts the child's interest. In addition, when the child is calm, the collection unit can also preferentially collect information that is useful for learning. Furthermore, when the child is tired, the collection unit can also preferentially collect information that helps the child relax. This allows the collection unit to determine the priority of information to be collected according to the child's emotion and to perform more effective information collection. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the child's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of information.

[0079] When collecting information, the collection unit can preferentially collect highly relevant information taking into account the geographical location information of the parent. For example, the collection unit preferentially collects educational resources in the area where the parent lives. The collection unit can also collect information on nearby educational events and workshops based on the geographical location of the parent. Furthermore, the collection unit can also collect information on appropriate educational methods taking into account the cultural background related to the geographical location of the parent. This allows the collection unit to preferentially collect highly relevant information taking into account the geographical location information of the parent. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the geographical location data of the parent to the generation AI and cause the generation AI to collect highly relevant information.

[0080] The collection unit can analyze the parent's social media activity and collect related information when collecting information. For example, the collection unit can analyze educational resources shared by the parent on social media and collect related information. The collection unit can also collect information on educational experts and organizations followed by the parent. Furthermore, the collection unit can also collect related educational information based on topics in which the parent has shown interest on social media. This allows the collection unit to analyze the parent's social media activity and efficiently collect related information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the parent's social media data to the generation AI and cause the generation AI to collect related information.

[0081] The analysis unit can estimate the child's emotion and adjust the expression method of the analysis based on the estimated emotion of the child. For example, the analysis unit provides a detailed analysis result when the child is relaxed. In addition, the analysis unit can also provide a visually attractive analysis result when the child is excited. Furthermore, the analysis unit can provide a concise and to the point analysis result when the child is tired. Thereby, the analysis unit can adjust the expression method of the analysis according to the child's emotion and provide a more understandable analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the child's emotion data to the generation AI and cause the generation AI to adjust the expression method of the analysis.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the child's personality and interests during the analysis. For example, if the child is curious, the analysis unit performs a detailed analysis to promote deep understanding. In addition, if the child lacks concentration, the analysis unit can perform a concise, to-the-point analysis. Furthermore, if the child has a strong interest in a particular field, the analysis unit can perform a detailed analysis specialized in that field. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the child's personality and interests and provide more appropriate analysis results. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the child's personality and interests to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the parent's educational policy during analysis. For example, if the parent supports Montessori education, the analysis unit applies an analysis algorithm based on the educational policy. In addition, if the parent supports Stiener education, the analysis unit can also apply an analysis algorithm based on the educational policy. In addition, if the parent supports Constructivism education, the analysis unit can also apply an analysis algorithm based on the educational policy. This allows the analysis unit to apply different analysis algorithms depending on the category of the parent's educational policy and provide more appropriate analysis results. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the parent's educational policy data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0084] The analysis unit can estimate the child's emotion and adjust the length of the analysis based on the estimated child's emotion. For example, when the child is relaxed, the analysis unit performs a detailed analysis and provides a longer report. In addition, when the child is in a hurry, the analysis unit can perform a short analysis that is concise and to the point. Furthermore, when the child is excited, the analysis unit can perform a short analysis that is visually appealing. This allows the analysis unit to adjust the length of the analysis according to the child's emotion and provide a more appropriate analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the child's emotion data to the generation AI and cause the generation AI to adjust the length of the analysis.

[0085] The analysis unit can determine the priority of the analysis based on the submission time of the collected information during the analysis. For example, the analysis unit can give priority to analyzing the latest information and provide the results quickly. The analysis unit can also give priority to analyzing important information and provide detailed results. Furthermore, the analysis unit can postpone analyzing old information and give priority to analyzing the latest information. This allows the analysis unit to determine the priority of the analysis based on the submission time of the collected information and provide the analysis results quickly. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the collected information to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the collected information during analysis. For example, the analysis unit preferentially analyzes information related to the child's personality and interests. The analysis unit can also preferentially analyze information related to the parent's educational policy. Furthermore, the analysis unit can also preferentially analyze information related to the child's learning style. This allows the analysis unit to adjust the order of analysis based on the relevance of the collected information and provide a more appropriate analysis result. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the collected information to the generation AI and cause the generation AI to adjust the order of analysis.

[0087] The suggestion unit can estimate the child's emotion and adjust the way of expressing the suggestion based on the estimated emotion of the child. For example, when the child is relaxed, the suggestion unit provides detailed suggestions to deepen understanding. In addition, when the child is excited, the suggestion unit can also provide visually attractive suggestions. Furthermore, when the child is tired, the suggestion unit can also provide concise and to the point suggestions. In this way, the suggestion unit can adjust the way of expressing the suggestion according to the child's emotion and provide a more understandable suggestion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the child's emotion data to the generation AI and cause the generation AI to adjust the way of expressing the suggestion.

[0088] The suggestion unit can adjust the level of detail of the proposal based on the importance of the educational method at the time of suggestion. For example, the suggestion unit makes a detailed proposal for an important educational method. The suggestion unit can also make a concise proposal for an educational method with a low importance. Furthermore, the suggestion unit can make a detailed proposal for an educational method that has a significant effect on the growth of a child. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of the educational method and provide a more appropriate proposal. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the importance data of the educational method to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0089] The suggestion unit can apply different suggestion algorithms depending on the category of the educational method at the time of suggestion. For example, for the Montessori educational method, the suggestion unit applies a suggestion algorithm based on its characteristics. Furthermore, for the Stienner educational method, the suggestion unit can also apply a suggestion algorithm based on its characteristics. Furthermore, for the Constructivist educational method, the suggestion unit can apply a suggestion algorithm based on its characteristics. Thereby, the suggestion unit can apply different suggestion algorithms depending on the category of the educational method and provide a more appropriate suggestion. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input category data of the educational method to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0090] The suggestion unit can estimate the child's emotion and adjust the length of the suggestion based on the estimated child's emotion. For example, when the child is relaxed, the suggestion unit can provide a detailed suggestion and a longer report. In addition, when the child is in a hurry, the suggestion unit can provide a short, concise and to-the-point suggestion. In addition, when the child is excited, the suggestion unit can provide a short, visually appealing suggestion. This allows the suggestion unit to adjust the length of the suggestion according to the child's emotion and provide a more appropriate suggestion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the child's emotion data to the generation AI and cause the generation AI to adjust the length of the suggestion.

[0091] At the time of proposal, the suggestion unit can determine the priority of the proposal based on the submission time of the educational method. For example, the suggestion unit preferentially proposes the latest educational method. The suggestion unit can also preferentially propose important educational methods. Furthermore, the suggestion unit can postpone old educational methods and preferentially propose the latest educational methods. In this way, the suggestion unit can determine the priority of the proposal based on the submission time of the educational method and quickly provide appropriate proposals. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the submission time data of the educational method to the generation AI and cause the generation AI to execute the determination of the priority of the proposal.

[0092] The suggestion unit can adjust the order of suggestions based on the relevance of the educational methods when making suggestions. For example, the suggestion unit preferentially suggests educational methods related to the child's personality and interests. The suggestion unit can also preferentially suggest educational methods related to the parent's educational policy. Furthermore, the suggestion unit can also preferentially suggest educational methods related to the child's learning style. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the educational methods and provide more appropriate suggestions. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input relevance data of educational methods to the generation AI and cause the generation AI to adjust the order of suggestions.

[0093] The teaching unit can estimate the child's emotion and adjust the teaching method based on the estimated child's emotion. For example, when the child is relaxed, the teaching unit provides detailed instruction to deepen understanding. In addition, when the child is excited, the teaching unit can provide visually attractive instruction. Furthermore, when the child is tired, the teaching unit can provide concise and to the point instruction. This allows the teaching unit to adjust the teaching method according to the child's emotion and provide more effective instruction. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the teaching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the teaching unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the teaching method.

[0094] During instruction, the instruction unit can select the optimal instruction method by analyzing the child's past learning behavior. For example, the instruction unit preferentially incorporates into the instruction a learning method that the child has had success with in the past. The instruction unit can also avoid learning methods that the child has had difficulty with in the past and try a different method. Furthermore, the instruction unit can analyze the child's past learning behavior and select the most effective instruction method. This allows the instruction unit to analyze the child's past learning behavior and select the optimal instruction method. A part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the child's past learning behavior data into the generation AI and cause the generation AI to select the optimal instruction method.

[0095] The teaching unit can customize the means of instruction based on the child's current learning situation during instruction. The teaching unit, for example, adjusts the means of instruction to match the content that the child is currently learning. The teaching unit can also adjust the pace of instruction according to the child's learning progress. Furthermore, the teaching unit can select optimal teaching materials and resources based on the child's current learning situation. This allows the teaching unit to customize the means of instruction based on the child's current learning situation and provide more effective instruction. Some or all of the above-mentioned processing in the teaching unit may be performed, for example, using AI or may be performed without using AI. For example, the teaching unit can input the child's current learning situation data into the generation AI and cause the generation AI to customize the means of instruction.

[0096] The teaching unit can estimate the child's emotions and determine the priority of instruction based on the estimated child's emotions. For example, when the child is relaxed, the teaching unit gives priority to important content. In addition, when the child is excited, the teaching unit can also give priority to interesting content. Furthermore, when the child is tired, the teaching unit can also give priority to relaxing content. This allows the teaching unit to determine the priority of instruction according to the child's emotions and provide more effective instruction. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the teaching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the teaching unit can input the child's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of instruction.

[0097] The teaching unit can select the optimal teaching method by taking into account the child's geographical location information during teaching. The teaching unit, for example, uses educational resources in the area where the child lives to provide teaching. The teaching unit can also incorporate nearby educational events and workshops into the teaching based on the child's geographical location. Furthermore, the teaching unit can select an appropriate teaching method by taking into account the cultural background related to the child's geographical location. This allows the teaching unit to select the optimal teaching method by taking into account the child's geographical location information. Some or all of the above-mentioned processing in the teaching unit may be performed, for example, using AI or may be performed without using AI. For example, the teaching unit can input the child's geographical location data into the generation AI and cause the generation AI to select the optimal teaching method.

[0098] During instruction, the guidance unit can analyze the child's social media activity and suggest teaching methods. The guidance unit can, for example, analyze the interests and concerns shared by the child on social media and suggest related teaching methods. The guidance unit can also incorporate information about educational experts and organizations that the child follows into the instruction. Furthermore, the guidance unit can also suggest related teaching methods based on topics in which the child has shown interest on social media. This allows the guidance unit to analyze the child's social media activity and suggest more appropriate teaching methods. Some or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI or may be performed without using AI. For example, the guidance unit can input the child's social media data into the generation AI and have the generation AI execute the suggestion of teaching methods.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The education suggestion system can further collect health data of the child and reflect it in the suggestion of an education method. For example, the collection unit collects health data such as the child's sleep pattern, eating habits, and amount of exercise. The analysis unit can analyze the health data and suggest an optimal education method based on the child's health condition. The suggestion unit selects an education method that suits the child's physical condition, taking into account the health data. The instruction unit can adjust the timing and content of instruction based on the health data, and provide effective education while maintaining the child's health.

[0101] The education suggestion system can further collect data on the child's friendships and reflect it in the suggestion of an education method. For example, the collection unit collects data on the child's friendships and sociability. The analysis unit can analyze this data and suggest an optimal education method based on the child's sociability and friendships. The suggestion unit selects an education method that allows the child to build better human relationships, taking into account the friendship data. The instruction unit can provide guidance to foster the child's sociability by incorporating group activities and cooperative learning based on the friendship data.

[0102] The education suggestion system can further estimate the child's emotions and suggest an education method based on the estimated emotions. For example, the collection unit estimates emotions from the child's facial expressions, tone of voice, and behavioral patterns. The analysis unit can analyze the estimated emotion data and suggest an optimal education method based on the child's emotional state. The suggestion unit selects an education method that enhances the child's concentration when the child is relaxed and an education method that attracts the child's interest when the child is excited, taking into account the emotion data. The instruction unit can adjust the method and timing of instruction based on the emotion data and provide effective education according to the child's emotions.

[0103] The education suggestion system can further collect the stress level of the parent and reflect it in the suggestion of the education method. For example, the collection unit uses a questionnaire or a sensor to measure the stress level of the parent. The analysis unit can analyze these data and suggest the optimal education method based on the stress level of the parent. The suggestion unit selects an education method that the parent can comfortably practice, taking into account the stress level of the parent. The instruction unit can adjust the frequency and content of the instruction based on the stress level of the parent, and provide effective education while reducing the burden on the parent.

[0104] The education suggestion system can further collect data on the child's learning environment and reflect it in the suggestion of an education method. For example, the collection unit collects data on the child's learning environment. The analysis unit can analyze the data and suggest an optimal education method based on the child's learning environment. The suggestion unit selects an education method that takes into account the learning environment data to create an environment in which the child can easily concentrate. The instruction unit can adjust the method and timing of instruction based on the learning environment data to maximize the child's learning effect.

[0105] The education suggestion system can further estimate the child's emotions and set learning goals based on the estimated emotions. For example, the collection unit estimates emotions from the child's facial expressions, tone of voice, and behavioral patterns. The analysis unit can analyze the estimated emotion data and set appropriate learning goals based on the child's emotional state. The suggestion unit takes into account the emotion data and sets high goals when the child is relaxed and realistic goals when the child is tired. The instruction unit can adjust the learning goals based on the emotion data and provide effective learning according to the child's emotions.

[0106] The education suggestion system can further analyze changes in the parents' educational principles and reflect them in the proposed educational methods. For example, the collection unit collects educational methods that the parents have tried in the past and their results. The analysis unit can analyze this data and suggest the optimal educational method based on the changes in the parents' educational principles. The suggestion unit selects the educational method that is most suitable for the current principles, taking into account the changes in the parents' educational principles. The instruction unit can adjust the method and content of instruction based on the data on the changes in the parents' educational principles, and provide effective education that matches the parents' principles.

[0107] The education suggestion system can further estimate the child's emotions and select learning resources based on the estimated emotions. For example, the collection unit estimates emotions from the child's facial expressions, tone of voice, and behavioral patterns. The analysis unit can analyze the estimated emotion data and select appropriate learning resources based on the child's emotional state. The suggestion unit takes into account the emotion data and selects resources that increase the child's concentration when the child is relaxed and resources that attract the child's interest when the child is excited. The instruction unit can adjust the learning resources based on the emotion data to provide effective learning according to the child's emotions.

[0108] The education suggestion system can further analyze the child's learning history and reflect it in the suggestion of an education method. For example, the collection unit collects the child's past learning history. The analysis unit can analyze these data and suggest the optimal education method based on the child's learning history. The suggestion unit takes into account the learning history data and preferentially suggests learning methods that have been successful for the child in the past. The teaching unit can adjust the teaching method and content based on the learning history data to maximize the child's learning effect.

[0109] The education suggestion system can further estimate the child's emotions and monitor the learning progress based on the estimated emotions. For example, the collection unit estimates emotions from the child's facial expressions, tone of voice, and behavioral patterns. The analysis unit can analyze the estimated emotion data and monitor the learning progress based on the child's emotional state. The suggestion unit takes the emotion data into consideration and monitors the progress in detail when the child is relaxed and briefly when the child is tired. The instruction unit can adjust the learning progress based on the emotion data and provide effective learning according to the child's emotions.

[0110] The process flow of the second embodiment will be briefly described below.

[0111] Step 1: The collection unit collects information about children and parents. The collected information includes age, gender, learning history, interests, and home environment. In addition to collecting information through questionnaires and interviews, the collection unit can also collect behavioral data about children and parents using sensors. For example, the data can be collected by monitoring children's behavioral patterns and learning behavior with sensors. Step 2: The analysis unit analyzes the collected information. Machine learning algorithms, statistical analysis, and neural networks are used for the analysis. For example, the analysis unit analyzes the child's personality and interests based on the collected data, and analyzes the parents' educational policy. Step 3: The suggestion unit determines an appropriate educational method to be applied to the child from among multiple educational methods based on the analysis results obtained by the analysis unit. The suggestion unit suggests the optimal educational method based on the child's learning style and interests. Step 4: The teaching department provides instruction based on the teaching methods proposed by the suggestion department. The teaching department can provide individual instruction, group instruction, or online instruction. In addition, the teaching department monitors the children's reactions during the instruction process, and if necessary, provides feedback to the suggestion department and proposes changes to the teaching methods.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0114] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] For example, the collection unit can collect behavioral data of the child and the parent using the camera 42 and the microphone 38B of the smart device 14. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and can collect information through questionnaires and interviews. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a machine learning algorithm. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate educational method based on the analysis result. The teaching unit is realized by the control unit 46A of the smart device 14, and provides guidance based on the proposed educational method. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0123] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] For example, the collection unit can collect behavioral data of the child and the parent using the camera 42 and the microphone 238 of the smart glasses 214. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and can collect information through questionnaires and interviews. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a machine learning algorithm. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate teaching method based on the analysis result. The teaching unit is realized by the control unit 46A of the smart glasses 214, and provides guidance based on the proposed teaching method. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] For example, the collection unit can collect behavioral data of the child and the parent using the camera 42 and microphone 238 of the headset type terminal 314. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and can collect information through questionnaires and interviews. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a machine learning algorithm. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate educational method based on the analysis result. The teaching unit is realized by the control unit 46A of the headset type terminal 314, and provides guidance based on the proposed educational method. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] For example, the collection unit can collect behavioral data of the child and the parent using the camera 42 and microphone 238 of the robot 414. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and can collect information through questionnaires and interviews. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a machine learning algorithm. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and determines an appropriate teaching method based on the analysis result. The teaching unit is realized by the control unit 46A of the robot 414, and provides guidance based on the proposed teaching method. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0166] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0168] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0169] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0170] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0173] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0174] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0175] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0176] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0177] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0178] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0179] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0181] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0183] (Appendix 1) A collection department that collects information about children and parents; an analysis unit that analyzes the information collected by the collection unit; a suggestion unit that suggests an educational method to be applied to the child based on the analysis result obtained by the analysis unit; A teaching unit that provides teaching based on the educational method proposed by the suggestion unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect child and parent behavioral data and interview results 2. The system of claim 1, (Appendix 3) The suggestion unit, Based on the analysis result obtained by the analysis unit, an appropriate educational method to be applied to the child is determined from among a plurality of educational methods. 2. The system of claim 1, (Appendix 4) The leadership team: Monitor the child's response during the teaching process, provide feedback to the teacher as necessary, and suggest changes to the teaching method. 2. The system of claim 1, (Appendix 5) The analysis unit is Analyze the collected information using machine learning algorithms 2. The system of claim 1, (Appendix 6) The analysis unit is Analyzing the character, personality, and interests of the child and the character, personality, and educational policy of the parent based on the information collected by the collection unit. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring a child's emotions and adjusting the timing of information collection based on the estimated child's emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze the history of parents' educational policies and select the appropriate method of collecting information 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting information, filter it based on the child's current learning status and areas of interest. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Inferring a child's emotions and prioritizing the information to be collected based on the inferred emotions of the child 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting information, the geographic location of the parent is taken into consideration to prioritize collection of relevant information. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting information, analyze the social media activity of parents and collect relevant information. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer the child's emotions and adjust the way the analysis is presented based on the estimated child's emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the child's personality and interests 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of parental education policy. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer the child's emotions and adjust the length of the analysis based on the estimated child's emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when collected information was submitted 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of the information collected 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer the child's emotions and adjust the way the suggestions are presented based on the child's emotions 2. The system of claim 1, (Appendix 20) The suggestion unit, When making suggestions, adjust the detail of the suggestions based on the importance of the teaching method 2. The system of claim 1, (Appendix 21) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of teaching method. 2. The system of claim 1, (Appendix 22) The suggestion unit, Estimate the child's emotions and adjust the length of the suggestions based on the estimated child's emotions 2. The system of claim 1, (Appendix 23) The suggestion unit, At the time of submission, we will prioritize proposals based on when the teaching method was submitted. 2. The system of claim 1, (Appendix 24) The suggestion unit, When making suggestions, adjust the order of suggestions based on the relevance of the teaching method 2. The system of claim 1, (Appendix 25) The leadership team: Estimate the child's emotions and adjust teaching methods based on the estimated child's emotions 2. The system of claim 1, (Appendix 26) The leadership team: When teaching, analyze the child's past learning behavior to select the most appropriate teaching method. 2. The system of claim 1, (Appendix 27) The leadership team: During instruction, customize instructional methods based on the child's current learning situation 2. The system of claim 1, (Appendix 28) The leadership team: Estimate the child's emotions and prioritize instruction based on the estimated child's emotions 2. The system of claim 1, (Appendix 29) The leadership team: When teaching, the optimal teaching method is selected taking into account the child's geographic location information. 2. The system of claim 1, (Appendix 30) The leadership team: Analyze children's social media activity and suggest teaching methods during instruction 2. The system of claim 1, [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects information about children and parents; an analysis unit that analyzes the information collected by the collection unit; a suggestion unit that suggests an educational method to be applied to the child based on the analysis result obtained by the analysis unit; A teaching unit that provides teaching based on the educational method proposed by the suggestion unit. A system characterized by:

2. The collecting unit includes: Collect child and parent behavioral data and interview results 2. The system of claim 1 .

3. The suggestion unit, Based on the analysis result obtained by the analysis unit, an appropriate educational method to be applied to the child is determined from among a plurality of educational methods.

2. The system of claim 1 .

4. The leadership team: Monitor the child's response during the teaching process, provide feedback to the teacher as necessary, and suggest changes to the teaching method.

2. The system of claim 1 .

5. The analysis unit is Analyze the collected information using machine learning algorithms 2. The system of claim 1 .

6. The analysis unit is Analyzing the character, personality, and interests of the child and the character, personality, and educational policy of the parent based on the information collected by the collection unit.

2. The system of claim 1 .

7. The collecting unit includes: Inferring a child's emotions and adjusting the timing of information collection based on the estimated child's emotions 2. The system of claim 1 .

8. The collecting unit includes: Analyze the history of parents' educational policies and select the appropriate method of collecting information 2. The system of claim 1 .

9. The collecting unit includes: When collecting information, filter it based on the child's current learning status and areas of interest.

2. The system of claim 1 .

10. The collecting unit includes: Inferring a child's emotions and prioritizing the information to be collected based on the inferred emotions of the child 2. The system of claim 1 .

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